Last commit on July 27, 2026·Created on June 18, 2026
huang-sir1/radiology-skills
“A framework for designing and documenting medical imaging AI studies for peer-reviewed publication.”
Combined rank
#198
across all skills
Stars
1.6k
7d change unavailable
Forks
17
Watchers
1
Traction scoreGitHub stars can be faked, so popularity alone can be misleading. Traction Score looks for broader signs of real attention, adoption, and active maintenance.
TL;DR
This skill provides structured guidance for planning, auditing, and writing radiomics and deep learning studies intended for high-impact medical journals. It assists researchers in navigating the rigorous requirements of venues such as RSNA, Nature-portfolio, and The Lancet.
It focuses on the technical precision of imaging protocols, ensuring that lesion-selection strategies and reader protocols are documented with the geometric integrity and reproducibility required for clinical validation.
WHO IT'S FOR
Medical imaging researchers
designing ROI and mask annotation protocols
Radiology academic writers
verifying citations for imaging journals
Medical AI engineers
validating geometric integrity of segmentation masks
Clinical trial investigators
establishing inter-observer agreement and reproducibility
Want to install it?
Clone to Codex · Manual CopyRepository instructions